Short answer: Both platforms can see AI activity, so the real question is which problem you are solving. If the pain is last-mile user activity in browsers, SaaS, desktop AI apps, and IDEs, LayerX is a credible fit. If the pain is governing enterprise AI adoption end to end, across prompts and responses, accounts and tenants, intent, copilots and coding assistants, and agents, MCP, and downstream tool execution, Aurascape is the stronger fit.

LayerX helps security teams control how users interact with AI. Aurascape helps security teams govern what happens across the AI workflow. When AI is just a chat window, last-mile controls may be enough. Enterprise AI is no longer just a chat window.

Employees use AI inside SaaS apps. Developers use tools like Cursor, Claude Code, GitHub Copilot, and Windsurf inside IDEs and terminals. Copilots inherit access to enterprise data. Agents call tools and retrieve information across systems. MCP turns AI from a conversational interface into an execution layer. With 83% of organizations planning to deploy AI agents and only 31% saying they are fully equipped to control and secure them (Cisco AI Readiness Index, 2025), the gap between watching AI use and governing it keeps widening.

Last updated: June 24, 2026.

If Your Pain Is Browser and Endpoint AI Activity, LayerX Is Credible

LayerX positions around AI usage control and browser security, and it is a credible fit for browser AI security and last-mile control. It suits organizations focused on browser-based AI use, SaaS activity, browser extensions, agentic browsers, desktop AI apps, IDEs, and endpoint workflows. Its browser extension secures browser-based interactions, its endpoint agent extends coverage to desktop AI apps, IDEs, IDE extensions, and on-device agents, and its published controls include AI DLP, AI access control, AI misuse prevention, AI browser protection, and AI IDE and plugin controls. LayerX also emphasizes channel-based deployment and fast rollout without network architecture changes, and markets a secure enterprise browser approach that works across common browsers.

Roadmap is part of that picture. In May 2026, Akamai announced a definitive agreement to acquire LayerX for about $205 million, with the transaction expected to close in Q3 2026, and Akamai said LayerX employees, including its co-founders, will join Akamai’s Zero Trust organization (Akamai, 2026). For teams standardizing on Akamai Zero Trust, that direction is a fit. Last-mile control, though, is not the whole AI security problem.

If Your Pain Is AI Governance Across the Full Workflow, Aurascape Is Stronger

Knowing that someone used AI is not enough. Enterprise AI governance means answering specific questions about any AI interaction:

1. Which AI tool was used.
2. Whether it was approved.
3. Whether the user was in a personal account or approved enterprise tenant.
4. What data was shared.
5. What the AI returned.
6. What action was attempted.
7. Whether policy was enforced.

Aurascape governs AI at the interaction itself. It decodes AI traffic natively, including WebSockets, QUIC, Protobuf, JSON, RPC, APIs, and MCP, and applies policy by identity, intention, entitlement, and data sensitivity across the endpoint, network, and API planes (Aurascape, 2026). Policy can allow, coach, warn, block, or redact, and redirect a user from a personal account to an approved enterprise tenant. That is governance of the AI workflow, not only control of the user interaction.

For many teams, the first problem is not enforcement. It is knowing which sanctioned, unsanctioned, personal, enterprise, and embedded AI tools are actually in use.

Aurascape answers that with AI-native breadth. It catalogs more than 20,000 AI apps and agents with a 48-hour connector SLA, and classifies sensitive data in real time across more than 600 categories, including text, code, images, and video (Aurascape, 2026).

The breadth matters because AI is already everywhere. By 2025, 88% of organizations reported using AI in at least one business function (Stanford HAI, 2026), much of it outside the browser, and IBM found that 1 in 5 breached organizations reported a breach tied to shadow AI, which added about 670,000 dollars to the average breach (IBM, 2025). In one Aurascape deployment, a global Fortune 200 healthcare technology enterprise drove unsanctioned and long-tail AI access toward near zero across more than 60,000 users worldwide under one governance model (Aurascape, 2026).

If Your Pain Is Agentic AI, the Difference Gets Sharper

LayerX can govern user and local agent activity through browser and endpoint channels. The harder CISO problem is agent blast radius: what an agent can retrieve, what tools it can call, what systems it can touch, and whether a tool invocation was authorized. LayerX is built around interaction channels. Aurascape is built around the governed AI workflow, including the execution path. Aurascape discovers and secures local AI agents and their interactions, and governs the agent-to-tool execution path through signed, approved tool calls in governed workflows, rather than relying on channel-level visibility alone (Aurascape, 2026).

Agent risk is mostly internal and mostly unseen. The Cloud Security Alliance found that only 21% of organizations maintain a real-time inventory of active agents (Cloud Security Alliance, 2026), and that 82% already have unknown AI agents operating in their environment (Cloud Security Alliance, 2026). In one Aurascape deployment, a Fortune 100 insurance and financial enterprise tripled its AI agent integrations with no unauthorized data access while protecting more than 20,000 users (Aurascape, 2026).

If Your Pain Is MCP Bypass, Aurascape Has the Clearest Advantage

MCP creates a new control problem: agents can call tools, retrieve data, and chain actions across systems. Monitoring a user’s AI session is not enough if an agent can reach tools through an unapproved path. Censys observed more than 12,520 internet-accessible MCP services as of April 2026, and the Model Context Protocol does not require authentication by default, leaving most exposed services unauthenticated (Censys, 2026). OWASP ranks Prompt Injection as a top risk in its Top 10 for LLM Applications (LLM01) and lists Excessive Agency among the top ten (LLM06), both aimed at the tool-execution path (OWASP, 2025).

Aurascape’s Zero-Bypass MCP approach is built to make approved tool execution enforceable in governed workflows, not merely observable. The Zero-Bypass MCP Gateway cryptographically signs approved tool calls and blocks unsigned ones, fail-closed, so only verified actions reach a tool or model, and cross-call data lineage tracks data across chained actions (Aurascape, 2026). The contrast is at its sharpest here: channel-level visibility into AI sessions versus enforcing the execution path in governed workflows.

If Your Pain Is Compliance and Investigations, Aurascape Is the Better System of Record

A CISO does not want screenshots of AI activity. They want reconstructable evidence, because an AI investigation is rarely about a single prompt. LayerX shows what happened at the user interaction layer. Aurascape is designed to show what happened across the AI exchange: the tool, tenant, identity, input, output, entitlement, intent, data movement, policy decision, and agent action. Aurascape keeps interaction records for audit and effectiveness, governed by role-based access control (RBAC) for privacy, so security and compliance teams can investigate an exchange end to end. Aurascape is also SOC 2 Type II compliant and built on patented AI discovery, risk attribution, and multimodal data classification (Aurascape, 2026).

Evidence at scale does not require a long rollout. In one Aurascape deployment, a large transportation and logistics company went from proof of value to full deployment in about six weeks, starting with 400 users on day one and rolling out to 2,000, with sensitive-data interactions monitored across 100% of deployed users (Aurascape, 2026).

Which Platform Fits: A Side-by-Side LayerX vs Aurascape Comparison

The question is not whether LayerX or Aurascape can see AI activity. Both can. LayerX is strongest where the control question is, “What is the user doing with AI?” Aurascape is strongest where the control question is, “What is happening across the AI workflow, and what can the AI or agent do next?” This side-by-side LayerX vs Aurascape comparison answers the questions most relevant for CISOs and their teams.

Frequently Asked Questions

Is LayerX an AI security platform or a browser security platform?

LayerX is both. It originated as a secure enterprise browser platform and now markets AI usage control across browsers, AI web apps, SaaS, extensions, and endpoint-assisted desktop and IDE workflows. Its control point is the last-mile user interaction, where a person meets the tool. Aurascape, by contrast, is an AI-native interaction governance platform whose control point is the AI interaction itself.

Does LayerX cover IDEs and desktop AI apps?

Yes. The LayerX endpoint agent extends coverage beyond the browser to desktop AI apps, IDEs, IDE extensions, and on-device agents, while its extension secures browser-based interactions. Aurascape also covers desktop, command-line, and IDE AI, and adds intention-level policy and cryptographic agent tool-call enforcement on top of visibility.

How is Aurascape different from secure enterprise browser tools?

Secure enterprise browser tools govern AI where the browser or user interaction is the control point. Aurascape governs AI where the AI interaction itself is the control point, across prompts, responses, intent, entitlement, MCP connections, and tool calls, on the endpoint, network, and API planes, so coverage is not tied to a single browser or channel.

What is the difference between AI usage control and enterprise AI governance?

AI usage control governs how users interact with AI, usually at the user, browser, and endpoint channels, which is where LayerX is credible. Enterprise AI governance is broader: it spans tenants and entitlements, intent and mode, prompts and responses, coding assistants and agents, MCP and tool calls, and the evidence of what happened. Aurascape is built for that wider scope, governing the AI workflow and the execution path, not only the user interaction.

Which platform is better for MCP security?

Aurascape is the stronger fit for MCP security. Its Zero-Bypass MCP Gateway cryptographically signs approved tool calls and blocks unsigned ones, fail-closed, and it discovers and secures local AI agents and their tool connections. LayerX published agent coverage centers on browser-based and endpoint-assisted activity rather than a cryptographic MCP gateway.

Does Aurascape replace SSE, SASE, CASB, or DLP?

No. Aurascape is an additive layer that runs alongside existing SSE, SASE, CASB, DLP, and SWG controls and closes the AI interaction gap those tools were not built to govern, including agent tool calls, MCP connections, and the responses and artifacts an AI returns.

What is the best LayerX alternative for agentic AI security?

For agentic AI security, Aurascape is a strong LayerX alternative. As a LayerX competitor, it governs the agent-to-tool execution path inline with a Zero-Bypass MCP Gateway, discovers and secures local AI agents, and applies intention-based policy across employee, application, and agent workflows. LayerX strength is last-mile user interaction across browsers and supported endpoint workflows.

Last-Mile AI Control or Enterprise AI Governance

LayerX is a strong choice for securing AI activity at the browser and endpoint. Aurascape is the stronger choice when AI security becomes an enterprise governance problem, controlling what AI can see and return, which account and mode it runs in, what tool it invokes, and what evidence remains. This matters for teams trying to avoid AI governance sprawl: the goal is not another narrow control, but one AI-native layer for employee AI use, embedded AI, coding assistants, agents, MCP-connected systems, and audit evidence. Put plainly: LayerX is strong when the risk is the user’s interaction with AI. Aurascape is stronger when the risk is what AI can do next.

Aurascape governs the full AI workflow: which tool and tenant, what was sent and returned, what an agent retrieves and invokes, and what evidence remains, from one AI-native interaction layer across the endpoint, network, and API planes. Every deployment starts with a tailored demo scoped to your AI security gaps.

See how Aurascape governs AI use, agents, and MCP tool execution →